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This article is a Commentary onZhanget al. (2023),237: 780–792.

 

Abstract Forest mortality has been widely observed across the globe during recent episodes of drought and extreme heat events. But the future of forest mortality remains poorly understood. While the direct effects of future climate and elevated CO 2 on forest mortality risk have been studied, the role of lateral subsurface water flow has rarely been considered. Here we demonstrated the fingerprint of lateral flow on the forest mortality risk of a riparian ecosystem using a coupled plant hydraulics-hydrology model prescribed with multiple Earth System Model projections of future hydroclimate. We showed that the anticipated water-saving and drought ameliorating effects of elevated CO 2 on mortality risk were largely compromised when lateral hydrological processes were considered. Further, we found lateral flow reduce ecosystem sensitivity to climate variations, by removing soil water excess during wet periods and providing additional water from groundwater storage during dry periods. These findings challenge the prevailing expectation of elevated CO 2 to reduce mortality risk and highlight the need to assess the effects of lateral flow exchange more explicitly moving forward with forest mortality projections. 

By utilizing functional relationships based on observations at plot or field scales, water quality models first compute surface runoff and then use it as the primary governing variable to estimate sediment and nutrient transport. When these models are applied at watershed scales, this serial model structure, coupling a surface runoff sub‐model with a water quality sub‐model, may be inappropriate because dominant hydrological processes differ among scales. A parallel modeling approach is proposed to evaluate how best to combine dominant hydrological processes for predicting water quality at watershed scales. In the parallel scheme, dominant variables of water quality models are identified based entirely on their statistical significance using time series analysis. Four surface runoff models of different model complexity were assessed using both the serial and parallel approaches to quantify the uncertainty on forcing variables used to predict water quality. The eight alternative model structures were tested against a 25‐year high‐resolution data set of streamflow, suspended sediment discharge, and phosphorous discharge at weekly time steps. Models using the parallel approach consistently performed better than serial‐based models, by having less error in predictions of watershed scale streamflow, sediment and phosphorus, which suggests model structures of water quantity and quality models at watershed scales should be reformulated by incorporating the dominant variables. The implication is that hydrological models should be constructed in a way that avoids stacking one sub‐model with one set of scale assumptions onto the front end of another sub‐model with a different set of scale assumptions.

 

Understanding the genetic and physiological basis of abiotic stress tolerance under field conditions is key to varietal crop improvement in the face of climate variability. Here, we investigate dynamic physiological responses to water stressin silicoand their relationships to genotypic variation in hydraulic traits of cotton (Gossypium hirsutum), an economically important species for renewable textile fiber production.

In conjunction with an ecophysiological process‐based model, heterogeneous data (plant hydraulic traits, spatially‐distributed soil texture, soil water content and canopy temperature) were used to examine hydraulic characteristics of cotton, evaluate their consequences on whole plant performance under drought, and explore potential genotype × environment effects.

Cotton was found to have R‐shaped hydraulic vulnerability curves (VCs), which were consistent under drought stress initiated at flowering. Stem VCs, expressed as percent loss of conductivity, differed across genotypes, whereas root VCs did not. Simulation results demonstrated how plant physiological stress can depend on the interaction between soil properties and irrigation management, which in turn affect genotypic rankings of transpiration in a time‐dependent manner.

Our study shows how a process‐based modeling framework can be used to link genotypic variation in hydraulic traits to differential acclimating behaviors under drought.

 

Trees may survive prolonged droughts by shifting water uptake to reliable water sources, but it is unknown if the dominant mechanism involves activating existing roots or growing new roots during drought, or some combination of the two.

To gain mechanistic insights on this unknown, a dynamic root‐hydraulic modeling framework was developed that set up a feedback between hydraulic controls over carbon allocation and the role of root growth on soil–plant hydraulics. The new model was tested using a 5 yr drought/heat field experiment on an established piñon‐juniper stand with root access to bedrock groundwater.

Owing to the high carbon cost per unit root area, modeled trees initialized without adequate bedrock groundwater access experienced potentially lethal declines in water potential, while all of the experimental trees maintained nonlethal water potentials. Simulated trees were unable to grow roots rapidly enough to mediate the hydraulic stress, particularly during warm droughts. Alternatively, modeled trees initiated with root access to bedrock groundwater matched the hydraulics of the experimental trees by increasing their water uptake from bedrock groundwater when soil layers dried out.

Therefore, the modeling framework identified a critical mechanism for drought response that required trees to shift water uptake among existing roots rather than growing new roots.